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Abstract Snow conditions are changing rapidly across our planet, which has important implications for wildlife managers. In Alaska, USA, the later arrival of snow is challenging wildlife managers' ability to conduct aerial fall (autumn) moose (Alces alces) surveys. Complete snow cover is required to reliably detect and count moose using visual observation from an aircraft. With inadequate snow to help generate high‐quality moose survey data, it is difficult for managers to determine if they are effectively meeting population goals and optimizing hunting opportunities. We quantified past relationships and projected future trends between snow conditions and moose survey success across 7 different moose management areas in Alaska using 32 years (1987–2019) of moose survey data and modeled snow data. We found that modeled mean snow depth was 15 cm (SD = 11) when moose surveys were initiated, and snow depths were greater in years when surveys were completed compared to years when surveys were canceled. Further, we found that mean snow depth toward the beginning of the survey season (1 November) was the best predictor of whether a survey was completed in any given year. Based on modeled conditions, the trend in mean snow depth on 1 November declined from 1980 to 2020 in 5 out of 7 survey areas. These findings, coupled with future projections, indicated that by 2055, the delayed onset of adequate snow accumulation in the fall will prevent the completion of moose surveys over roughly 60% of Alaska's managed moose areas at this time of the year. Our findings can be used by wildlife managers to guide decisions related to the future reliability of aerial fall moose surveys and help to identify timelines for development of alternate measurement and monitoring methods. 

This project used cutting-edge soundscape observations and analyses to quantify the influence of changing environmental dynamics and increasing anthropogenic activity on the behavior and phenology of migratory caribou, waterfowl, and songbird communities in Arctic-boreal Alaska and northwestern Canada. We used acoustic and camera-trap monitoring methods to evaluate wildlife responses in novel and non-invasive ways across broad spatial ranges during crucial seasons. Our study combined field observations, modeling, and analyses included (1) soundscape measurements, (2) camera-trap observations, (3) automated soundscape analyses, (4) analyses of camera-trap caribou observations, (5) high-resolution modeling of environmental variables, and (6) statistical analyses of wildlife occupancy, diversity, and phenology. This “Environmental Data” dataset package describes and includes the high-resolution environmental variables used in this study. 

Repeated transects have become the backbone of spatially distributed ice and snow thickness measurements crucial for understanding of ice mass balance. Here we detail the transects at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) 2019–2020, which represent the first such measurements collected across an entire season. Compared with similar historical transects, the snow at MOSAiC was thin (mean depths of approximately 0.1–0.3 m), while the sea ice was relatively thick first-year ice (FYI) and second-year ice (SYI). SYI was of two distinct types: relatively thin level ice formed from surfaces with extensive melt pond cover, and relatively thick deformed ice. On level SYI, spatial signatures of refrozen melt ponds remained detectable in January. At the beginning of winter the thinnest ice also had the thinnest snow, with winter growth rates of thin ice (0.33 m month−1 for FYI, 0.24 m month−1 for previously ponded SYI) exceeding that of thick ice (0.2 m month−1). By January, FYI already had a greater modal ice thickness (1.1 m) than previously ponded SYI (0.9 m). By February, modal thickness of all SYI and FYI became indistinguishable at about 1.4 m. The largest modal thicknesses were measured in May at 1.7 m. Transects included deformed ice, where largest volumes of snow accumulated by April. The remaining snow on level ice exhibited typical spatial heterogeneity in the form of snow dunes. Spatial correlation length scales for snow and sea ice ranged from 20 to 40 m or 60 to 90 m, depending on the sampling direction, which suggests that the known anisotropy of snow dunes also manifests in spatial patterns in sea ice thickness. The diverse snow and ice thickness data obtained from the MOSAiC transects represent an invaluable resource for model and remote sensing product development. 

Abstract Upper Indus Basin (UIB) streamflow originates largely from glacier and snow melt in the upstream Himalaya, Karakoram, and Hindu Kush mountain ranges and is extremely vulnerable because of its projected climate changes, dense populations, and hydropolitical tensions. Accurate knowledge of streamflow constituents is required for resilient water resources management; this is precluded by a paucity of measurement as well as climatological and topographic complexity. Here we integrate citizen scientist acquired geochemical samples, collected from October 2018 through September 2019 in the Shimshal watershed of the Karakoram Mountains of Pakistan, with Sentinel‐1 (S1) synthetic aperture radar (SAR)‐derived wet snow maps, to better understand streamflow constituents for the high altitude and heavily glaciated catchment. We use Bayesian end‐member mixture analysis to separate river flows into baseflow and meltwater constituents, using fixed and time‐variant melt end‐member values. We compare river hydrograph separation results with S1 wet snow time series maps for the same timeframe. We then utilize S1 imagery to inform end‐member mixture analysis to separate meltwaters into snow and glacier melt. For the Shimshal catchment, we find that about 85% of annual river flows are derived from snow and glacier melt; 45% of annual flows are derived from snow melt and 40% glacier melt. Engaged and committed citizen scientists enabled geochemical sample collection and analysis on a significant temporal and spatial scale. In the future, co‐produced knowledge that both implements local expertise and that is also planned and utilized by diverse stakeholders may increase climatological awareness and resilience in the UIB. 

Inuit hunters and meteorologists alike pay close attention to weather and weather changes, with deep understandings. This paper describes a long-time research project based in Kangiqtugaapik (Clyde River), Nunavut, where a research team of Inuit and visiting scientists have combined information and knowledge from a community-based weather station network, on-going interviews and discussions, and extensive travel (both Arctic fieldwork and visits to southern universities) to co-produce knowledge related to human–weather relationships and weather information needs and uses in one Nunavut community. The project uses the concept of “HREVs”, human-relevant environmental variables — complex, synthesis variables that, used in conjunction with a host of social variables, assist in informing safe land travel and activities. This work, including linking Inuit knowledge and environmental modeling, can be expanded to not only understand human–weather relationships more broadly and in other locations but also provide insights into the process of building diverse research teams and knowledge co-production. Inuit angunasuktiit amma silalirijiit tamarmik ujjiqsuttiasuunguvut silamit amma silaup asijjiqpallianingani, tukisiumaniqarjuaqłutik. Una paippaangujuq unikkaarivuq akuniujumi qaujitasaqtaunirmut piliriangujumi Kangiqtugaapik (Clyde River), Nunavummi, qaujisaqtiujuni katinngajuni Inungni amma pularaqtunut qaujisaqtiujunut katirisimajuni uqausiksani amma qaujimaniujumi nunalingni−tunngavilingmi silalirivvingmi tusaumatittiniujumi, apiqsuqtaunginnaqtuni amma uqallangniujuni, amma aullaaqsimarjuaqłutik (tamakkit Ukiuqtaqtumi iniujumi piliriniujumi amma pulararniujunut qallunaat nunanganni silattuqsarvigjuangujunut) saqqitittiqatigiingnirmut qaujimaniujumi pijjutiqaqtumut inungnut−silamut piliriqatigiingniujuni amma silamut uqausiksani pijariaqarniujunut amma aturniujunut atausirmi Nunavummi nunaliujumi. Piliriangujuq atusuunguvuq isumagijauniujumi “HREVs”, inungnut-atuutilingnut avatimut ajjigiinnginniujunut – nalunaqtuni, katinniujuni isumagijauniujuni aaqqiksinirnut piliri−jusiujumi ajjigiinnginniujuni, atuqatiqaqłuni ilagijaujumi inuuqatigiingujunut ajjigiinnginniujunit, ikajuqsuisuunguvuq aaqqiksuinirmi attananngittumi nunami aullaarniujumi amma qanuiliurniujunut. Una piliriniujuq ilaqaqtumi kasuqatiqarnirmi inuit qaujimajanginni amma avatimut uukturautiqarnirmi, angigligiaqtaujunnaqpuq tukisiumanituangunngittumi inungt-silamut piliriqatigiingniujumi tauvunngaujjiniujumi ammalu asinginni iniujunut, kisiani tunisijunnaqpuq tukisirjuarniujuni piliriniujuni sananirmut ajjigiinngiruluujaqtuni qaujisaqtiujunut katinngajuni amma qaujimanirmut saqqitittiqatigiingniujumi. 

Abstract With a unique biogeophysical signature relative to other freshwater sources, meltwater from glaciers plays a crucial role in the hydrological and ecological regime of high latitude coastal areas. Today, as glaciers worldwide exhibit persistent negative mass balance, glacier runoff is changing in both magnitude and timing, with potential downstream impacts on infrastructure, ecosystems, and ecosystem resources. However, runoff trends may be difficult to detect in coastal systems with large precipitation variability. Here, we use the coupled energy balance and water routing model SnowModel‐HydroFlow to examine changes in timing and magnitude of runoff from the western Juneau Icefield in Southeast Alaska between 1980 and 2016. We find that under sustained glacier mass loss (−0.57 ± 0.12 m w. e. a⁻¹), several hydrological variables related to runoff show increasing trends. This includes annual and spring glacier ice melt volumes (+10% and +16% decade⁻¹) which, because of higher proportions of precipitation, translate to smaller increases in glacier runoff (+3% and +7% decade⁻¹) and total watershed runoff (+1.4% and +3% decade⁻¹). These results suggest that the western Juneau Icefield watersheds are still in an increasing glacier runoff period prior to reaching “peak water.” In terms of timing, we find that maximum glacier ice melt is occurring earlier (2.5 days decade⁻¹), indicating a change in the source and quality of freshwater being delivered downstream in the early summer. Our findings highlight that even in maritime climates with large precipitation variability, high latitude coastal watersheds are experiencing hydrological regime change driven by ongoing glacier mass loss. 

Abstract A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution. 

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.